Employer will accept a Ph.D in Computer Science, Statistics, Linguistics, Electrical Engineering, Mathematics, Economics, Physics, Operations Research, or a related scientific discipline, or a related field, followed by one year of experience in the job offered or one year of experience in a related occupation. Experience must include one year of experience in each of the followingbr br 1 Recommendation system algorithms Matrix Factorization, Factorization Machines, Stochastic Gradient Descent Online Learning, X also Y Recommendation, and Collaborative Filtering.br 2 Unsupervised machine learning algorithms Kmeans Clustering.br 3 Dimensionality reduction algorithms Principal Component Analysis.br 4 Feature engineering techniques Missing Data Imputation, Mean Imputation, Median Imputation, Logarithmic Transformation, MinMax Scaling, Standardization Scaling, Removing outliers, Date and Time Engineering.br 5 Model performance evaluation and measures techniques Root Mean Square Error, Confusion Matrix, Precision, Recall, F1score, NRscore, True Positive, False Positive, True Negative, and False Negativebr 6 Data analysis methods AB testing, Time Series Analysis, Quantitative Analysis, Data Sampling, and Regression Analysisbr 7 Database programming languages and tools SQL, PostgreSQL, HIVE, Spark, and DbVisualizerbr 8 Programming languages Scala, Python, and Sparkbr 9 Data Structures and Algorithmsbr 10 Software testing Unit Testing, Integration Testing, and ProductionRelease Testingbr 11 Distributed computing Apache Hadoop, Apache Spark, and Apache Hivebr 12 Version control tools Git and Bamboobr 13 Tools and software VIM, Linux Operating System, IntelliJ IDEA, and FileZilla.br br Must be available to work on projects at various, unanticipated sites throughout the U.S.

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